With these theories at hand (“Potential landscapes” / “attractor landscapes”), the authors’ “mental impasse” and “attentional overload” (along with strong Gamma-oscillations) would boil down to a neural network temporarily trapped (or “being stuck”) in crowded “local minima” (while searching for a global minimum, i.e., the most stable “solution”).

And Gamma-oscillations may have something to do with the neural system’s “finding (activation) of attractors...”

And the authors’ “hint” and “restructuring” would simply boil down to a “flexibilization” and “modulation” of cortical attractor landscapes via internal subcortical inputs (Alpha-oscillations for cortical threshold-lowerings?) or stochastic noise fluctuations (see Deco) or even via “hints” (from the external world, i.e., given by the researchers), which would all enable the system to shift away from that “impasse” or “crowded local minima” to the final “solution” or “global minimum” already being stored in some “long term-memory” or Potential landscape, so that the system would ultimately settle in the most stable attractor (being the “most meaningful” one).

In this sense, even the authors’ term “deeper understanding” may indeed be quite revealing for this (endo- or exogenously induced) shift from crowded (“not quite correct”) local minima to a unique (“meaningful”) global minimum or “solution”...

Alas, scientists have never studied “droodles” so far, where a (at first “meaningless”) visual stimulus suddenly turns into a “meaningful” one (after a visual or textual “hint” or “priming”, see also Dolan, Dehaene et al.).

In fact, when all these mentioned scientists (Dolan, Dehaene, Deco, Bhattacharya, et al. – following Freeman, Kelso, Thelen & Smith et al.) combined their different fields of expertise, they would perhaps be able to map the whole attractor dynamics of the brain in real-time and in full spatial resolution (by designing perhaps a clever invasive visual droodle-experiment for monkeys?).

And this mapping of attractor dynamics in vivo and in real-time would not only be Nobel-prize like, but it would also solve a lot of art historical and neuro-esthetic puzzles (including droodles).

Thus, best wishes and thanks to everybody in these fields (and landscapes)

RE: Problem-solving by attractor dynamics

Thanks a lot for your insightful comments. Sorry for replying a bit late. You have drawn nice analogies and tried to give the insightful problem solving a framework based on nonlinear dynamical system theory. I am personally not aware of any such earlier effort but its worth pursuing. However I know there are some speculations on the small world type of network structure underlying cognitive insight.

You mentioned the Droodles - it will be a good paradigm to study. A relevant paradigm will be perceptual learning where also a sudden or abrupt kind of learning takes place. Nakayama and his group at Harvard did explore such paradigm in order to investigate the sudden vs incremental learning.

It will definitely be worth combining these apparently distant concepts in order to better understand our ability of critical thinking. Atually critical thinking is indeed centered about finding the novel and elegant yet nonobvious combination between two or more distant ideas/representations.